How Does Technology Affect Brain Development – The developing brain in the digital age: a cross-sectional study of structural and functional correlates of screen time in adolescence.
The widespread use of screen-based devices in adolescence has sparked a debate about the positive and negative effects on adolescent well-being and development. With the aim of summarizing the existing literature on the relationship between screen time (including Internet-related addiction) and adolescent brain development, the present review summarizes evidence from 16 task-irrelevant and task-relevant neuroimaging studies published between 2010 and 2020. Results highlight three important messages: (i) frequent and prolonged use of screen-based media (including Internet-related addictive behaviors) in adolescence is associated with less efficient cognitive control systems, including default mode network and central executive network areas; is related; (ii) online activity acts as a strong reward for the brain and repeated screen time increases the tendency to seek short-term gratification; and (iii) neuroscientific research on the correlation between screen time and adolescent brain development is still in its infancy, and further evidence is needed, particularly regarding the underlying causal mechanisms. Methodological, theoretical, and conceptual implications are discussed.
How Does Technology Affect Brain Development
Today’s youth have grown up in the digital age. More than any previous generation, their lives are shaped by constant availability of online content and services, 24/7 ability to reach and be reached by others, and easy access to satisfying and personalized content and functionality on screen-based devices. The widespread prevalence of screen-based devices in the adolescent population, including laptops, tablets, and, in particular, smartphones, has raised concerns about the harmful clinical and psychological effects defined as excessive screen time (Domingues-Montanari, 2017). Time spent interacting with the screen for a specified period of time. However, according to a recent review of eighty reviews (Orben, 2020), the existing literature on screen time and well-being is characterized by great heterogeneity, with most studies relying on cross-sectional, self-report, and low-quality data. . In general, the relationship between any form of screen time and well-being, including social media use, has proven to be negative but small, and more rigorous and better-designed research is urgently needed to provide stronger evidence. Given the limitations highlighted in the previous review, it is important to look directly at the primary structure involved in screen media consumption and its effects: the brain. Over the past two decades, reviews of neuroimaging studies have been conducted to investigate the relationship between (excess) screen time and brain activity (Kuss and Griffiths, 2012; Brand et al., 2014, 2016, 2019; Meng et al., 2015 ; Cerniglia et al., 2017; Yao et al., 2017; Crone and Konijn, 2018). However, only two of them included the adolescent population, but they were based on a narrative approach – not a systematic one. While Cerniglia et al. (2017) focused on the description of Internet-addicted adolescents, including prevalence rates, clinical assessments, and types of interventions, while Crone and Konijn (2018) described various early perspectives on social media, including responses to online peers. Adolescent growth can be affected. Exclusion and acceptance, online peer influence, and emotion regulation. Three other reviews used a systematic approach (Kuss and Griffiths, 2012; Meng et al., 2015; Yao et al., 2017) to summarize studies on Internet addiction and Internet gaming disorder in adult samples. Furthermore, Brand et al. (2014, 2016, 2019) proposed the Person-Affect-Cognition-Execution (I-PACE) model to outline the psychological and neurobiological mechanisms behind the development and maintenance of addictive online behaviors, but a systematic literature review of existing studies without doing And without a specific focus on the adolescent brain. But none of them considered new forms of screen media (eg, smartphone use). At the same time, the majority of previous reviews summarize results from different neuroimaging techniques (ie, EEG, PET, SPECT, MRI, and fMRI) and tasks transferred to the online world (eg, online friend rejection).
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Although the literature on the relationship between screen media use and adolescent brain development is still in its infancy, a more thorough and systematic summary of research findings to date is now important to paint a comprehensive picture of what has been investigated and the gaps. The time ahead must be filled by research. Therefore, the current exploratory study is important to (1) systematically review the emerging evidence on the relationship between screen time and adolescent brain development and report how research on this topic is conducted and (2) identify research gaps and accordingly. highlight new and important research avenues, providing specific guidance for future work (Meshi et al., 2015; Munn et al., 2018). To facilitate a critical evaluation of the neuroimaging studies conducted on the topic, the present meta-review focuses on studies using a functional magnetic resonance imaging (fMRI) approach. These neurophysiological methods can capture complex neuronal changes that may occur in adolescents who spend a lot of time with screen devices and display problematic usage behaviors (Meng et al., 2015; Yao et al., 2017). ; Sharifat et al., 2018 ).
Adolescence, defined as the transition from childhood to adulthood (Sawyer et al., 2018), is a period of development in which brain regions undergo significant changes influenced by biological and environmental factors (Burnett et al., 2011; Larsen and Luna, 2018). In general, cognitive abilities that promote effective self-regulation have been reported to develop gradually during adolescence, with neural correlates related to information processing efficiency, for example, axonal myelination and higher-order cognitive functions, including the prefrontal. cortex (PFC), anterior cingulate cortex (ACC) and parietal area (Steinberg, 2008; Albert et al., 2013; Caballero et al., 2016). Initially underdeveloped, the cognitive control system gradually matures, which increases the ability of adolescents to self-regulate their behavior, especially their emotions (Albert et al., 2013; Casey and Caudle, 2013). Specifically, the cognitive/cold and affective/warm control systems are associated with distinct but interconnected subregions of the PFC, namely, the dorsolateral-prefrontal-cortex (DLPFC) and the orbitofrontal/ventromedial prefrontal cortex (OFC/VMPFC). Early maturation of the cognitive control system promotes the maturation process of emotion regulation during adolescence (Schweizer et al., 2020). Specifically, improved emotion regulation depends on additional connections of forebrain regions to the amygdala and striatum, regions involved in emotion and reward processing (Aldao et al., 2016). The affective-motivational system also changes in relation to puberty hormones, with multiple effects of androgens and estrogens on brain structures, including subcortical brain regions associated with emotional processing, sensitivity to social and emotional stimuli, motivation, and satisfaction (ie, the amygdala). . , hippocampus, nucleus accumbens-NAcc, caudate, putamen, and striatum including globus pallidus) (Goddings et al., 2014). An earlier maturing affective motivational system is also associated with greater dopaminergic activity, with new projections from mesolimbic to prefrontal regions. The parallel development of different brain regions, namely, frontoparietal and subcortical structures, considers adolescence as a specific period of imbalance in brain development best described by the “dual-system model” (Steinberg, 2010). According to this developmental model, the affective-motivational system matures in relation to the control system during the early years of adolescence and matures in young adulthood. The temporal gap between the maturation of the two systems leads to increased vulnerability and propensity for risk-taking and reward-seeking and novelty-seeking behaviors during middle adolescence (Willoughby et al., 2014), especially when social components are involved (Willoughby et al., 2014). (Galván, 2013). At the same time, adolescents are not yet able to fully respond to positive and negative situations with their behavior, a capacity that develops later with greater strength of cortical-subcortical connectivity associated with better cognitive performance (van Duijvenvoorde et al., 2016). ) and to increase the ability to assess the positive and negative emotional consequences of one’s behavior (Johnson et al., 2008).
During adolescence, time spent with parents and parental influence in general decreases and peers become more relevant (Steinberg, 2002). In search of more independence, young people prefer to live with friends or alone (Dijkstra and Veenstra, 2011). The Internet, accessible through a variety of screen-based devices, allows adolescents to “escape” from parents and, in general, everyday problems, to connect with peers (e.g. via instant messaging and social media applications such as WhatsApp, Messenger, Instagram ) provides many opportunities. , and Snapchat), or engaging in highly pleasurable activities (eg, listening to music, watching videos, playing online games). Therefore, the Internet plays an important role during this developmental period (Crone and Konijn, 2018).
Online communication and entertainment activities are particularly relevant to adolescents’ psychosocial autonomy, which is promoted by the development of self-identity and the ability to initiate and maintain meaningful relationships with others (Steinberg, 2002). Compared to childhood, adolescence is characterized by a larger social network, also known as a public, with more complex and hierarchical peer relationships (Garner et al., 2006). People usually promote their own values, including original style of clothing, speech, and behavior. To be part of this group, young people feel pressured to act accordingly (Dijkstra and Veenstra, 2011). Not surprisingly, they experience hypersensitivity to peer acceptance and rejection (Somerville, 2013). In fact, people can influence adolescent self-esteem due to social comparisons and social norms (Steinberg, 2002). In this case, interaction through social media
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